Solar Asset Manager

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

Oct 15, 2019

AUTHORS:
Ciro Lanzetta and Fabrizio Ruffini.
ABSTRACT:
The Mowgli feasibility study started in 2018 funded by the European Space Agency (ESA) with the involvement of the India Energy Storage Alliance (IESA) as stakeholder. The aim is to evaluate the technical and economic feasibility of satellite-based services for microgrids. Designed by i-EM, Mowgli is a solution that provides a set of services for optimal microgrid planning, designing and operational and maintenance (O&M) applications in urban and rural areas developing countries, with focus on India as a user case.

A Machine Learning model for long-term power generation forecasting at bidding zone level

A Machine Learning model for long-term power generation forecasting at bidding zone level

Sep 11, 2019

AUTHORS:
Michela Moschella, Alessandro Betti, Emanuele Crisostomi and Mauro Tucci.
ABSTRACT:
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and more recently many statistical or machine learning algorithms, and data-driven methods in general, are becoming subject of intense research. While generally the power research community focuses on power forecasting at the level of single plants, in a short future horizon of time, in this time we are interested in aggregated macro-area power generation (i.e., in a territory of size greater than 100000 km2) with a future horizon of interest up to 15 days ahead. Real data are used to validate the proposed forecasting methodology on a test set of several months.

A Decision Support System based on Earth observation exploitation for renewable energy plants management

A Decision Support System based on Earth observation exploitation for renewable energy plants management

May 12, 2019

AUTHORS:
Andrea Masini, Ciro Lanzetta, Giuseppe Leotta, Gian Lorenzo Giuliattini Burbui, Pasquale Guerrisi and Maria Luisa Lo Trovato.
ABSTRACT:
Nowadays remote sensing information can be exploited to support the management of renewable energy plants during all its life cycles. Earth observation satellite can provide a continuous monitoring of each location of the earth. Low-resolution and high-resolution imagery can be exploited to obtain accurate descriptions of the monitored scenarios/plants. Moreover, the use of unmanned air vehicle can provide complementary information to monitor important features not detectable with satellite sensors. i-EM and Flyby developed and tested a system, called 4D-REDSS (4D Renewable plants Decision Support System) aimed to exploit remote sensed data to advantage and support the management of solar plant during the construction and pre-commissioning phase.

Day-ahead hourly forecasting of power generation from photovoltaic plants

Day-ahead hourly forecasting of power generation from photovoltaic plants

Mar 22, 2019

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

Big data and predictive maintenance in PV – The state of the art

Big data and predictive maintenance in PV – The state of the art

Feb 13, 2019

AUTHORS:
Alessandro Betti, Fabrizio Ruffini, Lorenzo Gigoni and Antonio Piazzi.
ABSTRACT:
Big data-based predictive analytics techniques using artificial intelligence technologies offer exciting new possibilities in the field of solar operations and maintenance. The authors examine how the power of data can be harnessed to safeguard the technical and economic performance of the PV fleet.

Predictive maintenance in photovoltaic plants with big data approach

Jan 8, 2019

AUTHORS:
Alessandro Betti, Maria Luisa Lo Trovato, Fabio Salvatore Leonardi, Giuseppe Leotta, Fabrizio Ruffini and Ciro Lanzetta.
ABSTRACT:
This paper presents a novel and flexible solution for fault prediction based on data collected from
SCADA system. Fault prediction is offered at two different levels based on a data-driven approach: (a) generic
fault/status prediction and (b) specific fault class prediction, implemented by means of two different machine learning
based modules built on an unsupervised clustering algorithm and a Pattern Recognition Neural Network, respectively.
Model has been assessed on a park of six photovoltaic (PV) plants up to 10 MW and on more than one hundred inverter
modules of three different technology brands. The results indicate that the proposed method is effective in (a) predicting
incipient generic faults up to 7 days in advance with sensitivity up to 95% and (b) anticipating damage of specific fault
classes with times ranging from few hours up to 7 days. The model is easily deployable for on-line monitoring of
anomalies on new PV plants and technologies, requiring only the availability of historical SCADA and fault data, fault
taxonomy and inverter electrical datasheet.